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Ten Things You Should Know about the Dynamic Conditional Correlation Representation

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Author Info

  • Massimiliano Caporin

    ()
    (Department of Economics and Management "Marco Fanno", University of Padova, Via del Santo 33, 35123 Padova, Italy)

  • Michael McAleer

    ()
    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, 3000 DR Rotterdam, Netherlands
    Department of Quantitative Economics, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
    Institute of Economic Research, Kyoto University, Kyoto 606-8501, Japan)

Abstract

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.

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Bibliographic Info

Article provided by MDPI, Open Access Journal in its journal Econometrics.

Volume (Year): 1 (2013)
Issue (Month): 1 (June)
Pages: 115-126

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Handle: RePEc:gam:jecnmx:v:1:y:2013:i:1:p:115-126:d:26620

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Keywords: DCC representation; BEKK; GARCC; stated representation; derived model; conditional correlations; two step estimators; assumed asymptotic properties; filter;

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References

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  1. Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 10/03, University of Canterbury, Department of Economics and Finance.
  2. Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Documentos de Trabajo del ICAE, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico 2011-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  3. Monica Billio & Massimiliano Caporin & Michele Gobbo, 2006. "Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation," Applied Financial Economics Letters, Taylor and Francis Journals, Taylor and Francis Journals, vol. 2(2), pages 123-130, March.
  4. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  5. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(01), pages 232-261, February.
  6. Caporin, M. & McAleer, M.J., 2010. "Do We Really Need Both BEKK and DCC? A Tale of Two Multivariate GARCH Models," Econometric Institute Research Papers EI 2010-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  8. Matteo Manera & Alessandro Lanza & Michael McAleer, 2004. "Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns," Working Papers, Fondazione Eni Enrico Mattei 2004.72, Fondazione Eni Enrico Mattei.
  9. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, Elsevier, vol. 164(1), pages 45-59, September.
  10. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 11(01), pages 122-150, February.
  11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, Econometric Society, vol. 50(4), pages 987-1007, July.
  12. Maria Kasch & Massimiliano Caporin, 2008. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," "Marco Fanno" Working Papers 0065, Dipartimento di Scienze Economiche "Marco Fanno".
  13. McAleer, Michael & Chan, Felix & Hoti, Suhejla & Lieberman, Offer, 2008. "Generalized Autoregressive Conditional Correlation," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 24(06), pages 1554-1583, December.
  14. Sheppard, Kevin & Cappiello, Lorenzo & Engle, Robert F., 2003. "Asymmetric dynamics in the correlations of global equity and bond returns," Working Paper Series, European Central Bank 0204, European Central Bank.
  15. Robert Engle & Neil Shephard & Kevin Shepphard, 2008. "Fitting vast dimensional time-varying covariance models," OFRC Working Papers Series, Oxford Financial Research Centre 2008fe30, Oxford Financial Research Centre.
  16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(3), pages 339-50, July.
  17. Christian Hafner & Philip Hans Franses, 2009. "A Generalized Dynamic Conditional Correlation Model: Simulation and Application to Many Assets," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 28(6), pages 612-631.
  18. Massimiliano Caporin & Michael McAleer, 2008. "Scalar BEKK and indirect DCC," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 27(6), pages 537-549.
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Citations

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Cited by:
  1. Jean-David Fermanian & Hassan Malongo, 2013. "On the Stationarity of Dynamic Conditional Correlation Models," Working Papers, Centre de Recherche en Economie et Statistique 2013-26, Centre de Recherche en Economie et Statistique.
  2. Chia-Lin Chang & Hui-Kuang Hsu & Michael McAleer, 2014. "A Tourism Conditions Index," Tinbergen Institute Discussion Papers 2014-007/III, Tinbergen Institute.
  3. Christian M. Hafner & Michael McAleer, 2014. "A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 14/19, University of Canterbury, Department of Economics and Finance.
  4. Michael McAleer, 2014. "Discussion of “Principal Volatility Component Analysis” by Yu-Pin Hu and Ruey Tsay," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 14/09, University of Canterbury, Department of Economics and Finance.
  5. Jean-David Fermanian & Hassan Malongo, 2014. "On the stationarity of Dynamic Conditional Correlation models," Papers 1405.6905, arXiv.org.
  6. Christian M. Hafner & and Michael McAleer, 2014. "A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process," Tinbergen Institute Discussion Papers 14-087/III, Tinbergen Institute.

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